blind separation algorithm for audio signal based on genetic algorithm and neural network
DESCRIPTION
Blind Separation Algorithm for Audio Signal Based on Genetic Algorithm and Neural Network. 2008 International Symposium on Information Science and Engineering. Dahui Li , Ming Diao and Xuefeng Dai. Presenter: Jain_De ,Lee. OUTLINE. INTRODUCTION PROBLEM DESCRIPTION ALGORITHM DESCRIPTION - PowerPoint PPT PresentationTRANSCRIPT
2008 International Symposium on Information Science and Engineering
Presenter: Jain_De ,Lee
Dahui Li , Ming Diao and Xuefeng Dai
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OUTLINEINTRODUCTION
PROBLEM DESCRIPTION
ALGORITHM DESCRIPTION
SIMULATION EXPERIMENT
CONCLUSION
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INTRODUCTIONThe Core of Blind Separation Problem
† Getting separation matrix
Error Backpropagation Algorithm† Fall into Local optimal trap
ICA Based on Information Theory† Have better separation† Only appropriate for non-Gauss† Complicated computation and convergence
slowly
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INTRODUCTIONICA Based on Measurement of Non-Gaussian
† Has the quickly calculation† Good statistical characteristics and robustness† Separation result often inaccurate
Neural Network Algorithm and the Genetic Algorithm
† Have less restrictions on optimization problems† Not be continuous or differentiable
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PROBLEM DESCRIPTION
S(t): source signal vectorX(t): observation signal vector[aij]n×n : transmission matrix
Y(t): signal vector of the separation outputs
Composite Separation Model
X=AS[Wij]n×n
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ALGORITHM DESCRIPTION
Gen
etic
Alg
orith
m
output signal
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GENETIC ALGORITHM DESCRIPTIONGenetic Algorithm Operation
† Reproduction / Selection† Crossover† Mutation
Reproduction / Selection† roulette wheel selection† tournament selection
42.3%
22.7%
23.6%
5.6%
5.8%
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GENETIC ALGORITHM DESCRIPTIONCrossover
† Setting crossover probability(0.8~1)† Crossover types
‡ 1-point crossover‡ 2-point crossover‡ Mask crossover
Mutation† Setting mutation probability(0.01~0.08)
0 1
1
0 01 1 1
1 0 0 011
0 1
1
0 01 1 1
1 0 0 011
0 1
1
0 01 1 1
1 0 0 011
Mask 1 1 10000
10 1 1 00 10
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ALGORITHM DESCRIPTIONPretreatment
† Centering– m=E{x} 、 E{x-m}=0† Whitening –use of PCA(Principal Component
Analysis )
Generates Initial Separation Matrixes† Randomly generate 50 separation matrixes† Consist of chromosome of 8 bit binary code
Calculates y=wx
E{xxT}=EDET 、 z=Vx=ED-1/2ETx
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ALGORITHM DESCRIPTIONMakes y Centering and Whitening
Calculates the fitness values
Determine the signal whether Correct† TRUE– Output signal and end the process† FALSE– Take the crossover or mutation operation
Fitness function :
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SIMULATION EXPERIMENTExperimental Condition
† Data Sampling Frequency – 10 kHz† Audio Signal
† Transmission Matrix
Truck signalAgriculture car signal
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SIMULATION EXPERIMENTMixed Signal
Truck mixture signal
Agriculture car mixture signal
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SIMULATION EXPERIMENT
Error Backpropagation
Genetic Neural Network
Convergence
9000 5000
Convergence Speed
SLOW FAST
Degree of Convergen
ce not accuracy accuracy
The Convergence Speed of the Two Algorithms
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SIMULATION EXPERIMENTThe signal separation matrix w
Separate signals
Joint momentE(A,W-1)=0.0854
Truck separation signalAgriculture car separation signal
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CONCLUSIONThe algorithm has the characteristics of
convergence quickly and separation effectively
cross-operation and mutation operation lead to chain issues
Future research topic† The source signals number is less than that of
observation signals† Non-Gaussian noise† Pulsing signal
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